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There are various research strategies used for non-Hermitian systems, which typically involve introducing non-Hermitian terms to pre-existing Hermitian Hamiltonians. It can be challenging to directly design non-Hermitian many-body models…

Quantum Physics · Physics 2023-06-02 Ruohan Shen , Yuchen Guo , Shuo Yang

We show in the example of a one-dimensional asymmetric exclusion process that stationary states of models with parallel dynamics may be written in a matrix product form. The corresponding algebra is quadratic and involves three different…

Condensed Matter · Physics 2009-10-28 Haye Hinrichsen

Significant efforts have been devoted to manipulating topological states, which often manifest as localized modes at interfaces between distinct topological phases. In this work, we demonstrate a versatile approach to sculpting topological…

Full coherent control and generation of superpositions of the quantum harmonic oscillator are not only of fundamental interest but are crucial for applications in quantum simulations, quantum-enhanced metrology and continuous-variable…

Quantum Physics · Physics 2024-09-06 S. Saner , O. Băzăvan , D. J. Webb , G. Araneda , D. M. Lucas , C. J. Ballance , R. Srinivas

We introduce an iterative method named GPMR for solving 2x2 block unsymmetric linear systems. GPMR is based on a new process that reduces simultaneously two rectangular matrices to upper Hessenberg form and that is closely related to the…

Numerical Analysis · Mathematics 2021-11-16 Alexis Montoison , Dominique Orban

We demonstrate that it is possible to compensate for effects of strong linear loss when generating non-classical states by engineered nonlinear dissipation. We show that it is always possible to construct such a loss-resistant dissipative…

Quantum Physics · Physics 2013-08-02 D. Mogilevtsev , A. Mikhalychev , V. S. Shchesnovich , N. Korolkova

We propose automated augmented conjugate inference, a new inference method for non-conjugate Gaussian processes (GP) models. Our method automatically constructs an auxiliary variable augmentation that renders the GP model conditionally…

Machine Learning · Statistics 2020-02-27 Théo Galy-Fajou , Florian Wenzel , Manfred Opper

The multiresolution Gaussian process (GP) has gained increasing attention as a viable approach towards improving the quality of approximations in GPs that scale well to large-scale data. Most of the current constructions assume full…

Machine Learning · Statistics 2019-02-26 Jalil Taghia , Thomas B. Schön

Linear modal analysis is a useful and effective tool for the design and analysis of structures. However, a comprehensive basis for nonlinear modal analysis remains to be developed. In the current work, a machine learning scheme is proposed…

Machine Learning · Computer Science 2022-03-03 G. Tsialiamanis , M. D. Champneys , N. Dervilis , D. J. Wagg , K. Worden

We present a conjugate-gradient method for the ground-state optimization of projected entangled-pair states (PEPS) in the thermodynamic limit, as a direct implementation of the variational principle within the PEPS manifold. Our…

Strongly Correlated Electrons · Physics 2016-11-08 Laurens Vanderstraeten , Jutho Haegeman , Philippe Corboz , Frank Verstraete

We propose a simple method for constructing POVMs using any set of matrices which form an orthonormal basis for the space of complex matrices. Considering the orthonormal set of irreducible spherical tensors, we examine the properties of…

Quantum Physics · Physics 2015-05-04 Swarnamala Sirsi , Karthik Bharath , S. P. Shilpashree

Undirected graphical models are compact representations of joint probability distributions over random variables. To solve inference tasks of interest, graphical models of arbitrary topology can be trained using empirical risk minimization.…

Machine Learning · Computer Science 2020-10-23 Adarsh K. Jeewajee , Leslie P. Kaelbling

Nonlocal sets of orthogonal product states (OPSs) are widely used in quantum protocols owing to their good property. Thus a lot of attention are paid to how to construct a nonlocal set of orthogonal product states though it is a difficult…

Quantum Physics · Physics 2020-07-29 G. B. Xu , D. H. Jiang

We explain how to construct matrix product stationary states which are composed of finite-dimensional matrices. Our construction explained in this article was first presented in a part of [Hieida and Sasamoto:J. Phys. A: Math. Gen. 37…

Statistical Mechanics · Physics 2016-11-23 Yasuhiro Hieida , Tomohiro Sasamoto

Off-diagonal geometric phases have been developed in order to provide information of the geometry of paths that connect noninterfering quantal states. We propose a kinematic approach to off-diagonal geometric phases for pure and mixed…

Quantum Physics · Physics 2016-08-16 D. M. Tong , Erik Sjöqvist , Stefan Filipp , L. C. Kwek , C. H. Oh

We study a class of matrices with a rank-1 interconnection structure, and derive a simple necessary and sufficient condition for diagonal stability. The underlying Lyapunov function is used to provide sufficient conditions for diagonal…

Optimization and Control · Mathematics 2021-05-21 John W. Simpson-Porco , Nima Monshizadeh

Based on maximally entangled states, we explore the constructions of mutually unbiased bases in bipartite quantum systems. We present a new way to construct mutually unbiased bases by difference matrices in the theory of combinatorial…

Quantum Physics · Physics 2022-10-05 Yajuan Zang , Zihong Tian , Hui-Juan Zuo , Shao-Ming Fei

Given a finite irreducible set of real $d \times d$ matrices $A_1,\ldots,A_M$ and a real parameter $s>0$, there exists a unique shift-invariant equilibrium state associated to $(A_1,\ldots,A_M,s)$. In this article we characterise the…

Dynamical Systems · Mathematics 2016-10-06 Ian D. Morris

We study fermionic matrix product operator algebras and identify the associated algebraic data. Using this algebraic data we construct fermionic tensor network states in two dimensions that have non-trivial symmetry-protected or intrinsic…

Strongly Correlated Electrons · Physics 2017-12-11 Nick Bultinck , Dominic J. Williamson , Jutho Haegeman , Frank Verstraete

In this paper, we propose a model-free unsupervised learning approach to forecast real-time locational marginal prices (RTLMPs) in wholesale electricity markets. By organizing system-wide hourly RTLMP data into a 3-dimensional (3D) tensor…

Machine Learning · Computer Science 2020-11-11 Zhongxia Zhang , Meng Wu